System And Method For Using Image Data To Determine A Direction Of An Actor

ABSTRACT

Example systems and methods are disclosed for determining the direction of an actor based on image data and sensors in an environment. The method may include receiving point cloud data for an actor at a location within the environment. The method may also include receiving image data of the location. The received image data corresponds to the point cloud data received from the same location. The method may also include identifying a part of the received image data that is representative of the face of the actor. The method may further include determining a direction of the face of the actor based on the identified part of the received image data. The method may further include determining a direction of the actor based on the direction of the face of the actor. The method may also include providing information indicating the determined direction of the actor.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 14/919,700 filed on Oct. 21, 2015, entitled “System And MethodFor Using Image Data To Determine A Direction Of An Actor,” the contentsof which are incorporated herein by reference, as if fully set forth inthis description.

BACKGROUND

Physical spaces may be used for retail, manufacturing, assembly,distribution, office space, and/or other purposes. The design andoperation of these physical spaces is becoming more intelligent, moreefficient, and more intuitive. As technology becomes increasinglyprevalent in modern life, using technology to enhance physical spacesbecomes more apparent. Thus, a demand for enhanced physical spaces hasincreased innovation in sensing techniques, data processing, software,and user interface design.

SUMMARY

Example systems and methods may provide for determining a direction ofan actor based on image data and sensors within an environment. Theenvironment may include sensors, such as LIDAR sensors, that receivepoint cloud data representative of an actor at a location. Theenvironment may also include image capture devices, such as a camera,that receives image data of the location. The image data may include theface of the actor. The system may determine a direction of the actorbased on the point cloud data and the image data with the face of theactor.

After receiving the point cloud data and the image data of the location,the system may link the image data to the point cloud data based on thelocation. The system may then identify a part of the image data thatincludes a face of the actor. The system may determine a direction thatthe face of the actor is oriented. Based on the determined direction,the system may infer a direction of the actor. In some cases, the systemmay determine that the direction of the actor is the same as thedirection of the face, while in other cases, the system may determinethat the two directions are different.

In one example, a method is provided that includes receiving point clouddata for an actor at a first location in an environment, wherein thepoint cloud data includes a plurality of points representative of theactor. The method may also include receiving image data corresponding tothe point cloud data for the actor based on the image data beingrepresentative of the first location in the environment. The method mayadditionally include identifying a portion of the received image datathat is representative of a face of the actor. The method may alsoinclude determining a direction of the face of the actor based on theidentified portion of the received image data. The method may furtherinclude determining a direction of the actor based on the direction ofthe face of the actor. The method may even further include providinginformation indicating the determined direction of the actor.

In an additional example, a non-transitory computer readable medium isprovided that stores instructions that are executable by one or morecomputing devices. When the instructions are executed, the instructionscause the one or more computing devices to perform functions thatinclude receiving point cloud data for an actor at a first location inan environment, wherein the point cloud data includes a plurality ofpoints representative of the actor. The functions may also includereceiving image data corresponding to the point cloud data for the actorbased on the image data being representative of the first location inthe environment. The functions may also include identifying a portion ofthe received image data that is representative of a face of the actor.The functions may also include determining a direction of the face ofthe actor based on the identified portion of the received image data.The functions may further include determining a direction of the actorbased on the direction of the face of the actor. The functions mayinclude providing information indicating the determined direction of theactor.

In another example, a robotic device is disclosed that includes one ormore processors and a memory that stores instructions that are executedby the one or more processors. When executed, the instructions cause therobotic device to perform functions that include receiving point clouddata for an actor at a first location in an environment, wherein thepoint cloud data includes a plurality of points representative of theactor. The functions may also include receiving image data correspondingto the point cloud data for the actor based on the image data beingrepresentative of the first location in the environment. The functionsmay also include identifying a portion of the received image data thatis representative of a face of the actor. The functions may also includedetermining a direction of the face of the actor based on the identifiedportion of the received image data. The functions may further includedetermining a direction of the actor based on the direction of the faceof the actor. The functions may even further include adjusting operationof the robotic device based on the determined direction of the actor.

In a further example, a system may include means for receiving pointcloud data for an actor at a first location in an environment, whereinthe point cloud data includes a plurality of points representative ofthe actor. The system may also include means for receiving image datacorresponding to the point cloud data for the actor based on the imagedata being representative of the first location in the environment. Thesystem may additionally include means for identifying a portion of thereceived image data that is representative of a face of the actor. Thesystem may also include means for determining a direction of the face ofthe actor based on the identified portion of the received image data.The system may further include means for determining a direction of theactor based on the direction of the face of the actor. The system mayeven further include means for providing information indicating thedetermined direction of the actor.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the figures and the followingdetailed description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a configuration of a system for determining adirection of an actor based on image data and sensors in an environment,according to an example embodiment.

FIG. 2A illustrates an example environment with an actor, according toan example embodiment.

FIG. 2B illustrates another example environment with an actor and arobotic device, according to an example embodiment.

FIG. 3A illustrates an example point cloud representative of an actor,according to an example embodiment.

FIG. 3B illustrates example image data representative of a locationwithin an environment, according to an example embodiment.

FIG. 4 illustrates another example environment with an actor, accordingto an example embodiment.

FIG. 5 is a block diagram of an example method, according to an exampleembodiment.

DETAILED DESCRIPTION

Example methods and systems are described herein. Any example embodimentor feature described herein is not necessarily to be construed aspreferred or advantageous over other embodiments or features. Theexample embodiments described herein are not meant to be limiting. Itwill be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

Furthermore, the particular arrangements shown in the Figures should notbe viewed as limiting. It should be understood that other embodimentsmight include more or less of each element shown in a given Figure.Further, some of the illustrated elements may be combined or omitted.Yet further, an example embodiment may include elements that are notillustrated in the Figures.

For a system that detects actors (such as people, robots, etc.) within aspace (such as a 20 meter by 20 meter room), it may be useful todetermine additional information about one or more actors within thespace. For example, identifying a direction that an actor in the spaceis oriented may be useful. Determining this information, however, may bechallenging for at least three reasons.

First, the system may detect people within the space using acquiredsensor data (such as sensor data from LIDAR sensors) that provideslimited information about the actor. Second, the collected sensor datamay be sparse: there may not be many data points collected by thesystem. Third, the density of the acquired data points may benon-uniform. For example, some areas may have high point density whileother areas have low point density. Thus, it may be challenging todetermine additional information about people detected within the space.

To overcome this challenge, a method may be executed by a system relyingon one or more LIDAR sensors and one or more image capturing devices(such as cameras). The system may be able to detect people and/or facesof people to determine the direction of the detected face and/or actor.The method may begin by obtaining point cloud data representative of anactor at a location in a space from LIDAR sensors within the space. Themethod may continue by obtaining image data of the location andidentifying a portion of the image data that includes a face of theactor at the location. The image data may be obtained from one or morecameras in the space.

The method may continue by mapping the image data of the actor to thecorresponding point cloud data for the actor. The method may theninclude determining the direction of the detected face of the actorbased on the image data of the face. After this step, the method mayproceed to determining the direction of an actor based on the directionof the face of the actor and the point cloud data for the actor.

By executing the above method, the system can determine informationabout detected actor(s) within the space. First, the system maydetermine the direction that the face of an actor is oriented. Next, thesystem may infer the direction that an actor is oriented or moving basedon the determined direction of the face of the actor. Additionally, thesystem may determine the direction of a body of the actor. In somecases, the system may rely on a bounding box drawn around the torso ofthe actor to determine the direction the torso is oriented. The systemmay also determine that the direction that the face is oriented differsfrom the direction that the torso is oriented. Other information mayalso be determined by the system.

The system may rely on one or more LIDAR sensors to acquire point clouddata about the actor(s) within the space. The point cloud data mayinclude a plurality of points, each of which represents a point on thesurface of the actor. The LIDAR data acquired may be sparse, in someembodiments. The point cloud data may also have a non-uniform density.Because the point cloud data is sparse with a non-uniform density, thesystem may rely on other types of sensors to acquire sensor data to makeinferences about the actors within the space.

The system may rely on one or more camera sensors to detect faces of theone or more actors within the space. Based on the detected faces, thesystem may then make inferences regarding the direction the face of theactor is oriented, the direction the actor is oriented, the directionthe actor is moving, and/or the direction the torso of the actor isoriented. The system may rely on one or more image capture devices, suchas cameras. Multiple cameras may be used to acquire image data andreduce errors present in the captured image data. Various types of imagecapture sensors can be used, including PTZ cameras (pan-tilt-zoom),stationary cameras, moving cameras, RGB cameras, infrared cameras,and/or other types of image capture devices.

Mapping image data including a face of an actor may be preferred for atleast two reasons. First, image data of a face of an actor that may be arelatively easy portion of image data to recognize within image datarepresentative of an actor. In particular, image data of facial featuresof an actor may be relatively easy to distinguish from other image datarepresentative of an actor. Second, image data of a face of an actor mayprovide rich, detailed information about the actor, such as thedirection the face of the actor is oriented. For at least these tworeasons, image data of the face of the actor may be helpful for makinginferences about the point cloud data representative of the actor.

The inferred information (such as the direction an actor's face isoriented) may be useful for various applications. For example, a roboticdevice operating in the space may require information about thedirection of an actor's face to execute tasks. In particular, while therobot may navigate the space by using information about the presence ofthe actor in the space, the robot may execute tasks for telepresence(such as communicating with the actor) using information indicating thedirection that the actor's face is oriented.

For another example, videoconferencing may be more effective by usinginformation indicating the direction an actor's face is oriented. Inparticular, a remote camera angle and/or location may be adjusted basedon changes in the direction that an actor's face is oriented.Specifically, an actor at a first location may move their head to theright to get a better view of a remote location on a display. Inresponse to the head movement, a camera at the remote location may moveto the right to provide a better view of the remote location on thedisplay. Inferred information determined based on image data, includinga face of an actor, may be useful for other applications as well.

FIG. 1 shows an example physical space 100 having one or more sensors102-103. A physical space may define a portion of an environment inwhich people, objects, and/or machines may be located. The physicalspace may take on a two-dimensional or a three-dimensional form and maybe used for various purposes. For instance, the physical space may beused as a retail space where the sale of goods and/or services iscarried out between individuals (or businesses) and consumers. Whilevarious aspects of the disclosure are discussed below in the context ofa general space, example implementations are not limited to generalspaces and may extend to a variety of other physical spaces such asretail spaces, manufacturing facilities, distribution facilities, officespaces, shopping centers, festival grounds, and/or airports, among otherexamples. Although only one physical space 100 is shown in FIG. 1,example implementations may be carried out in the context of a pluralityof physical spaces.

Example sensors in a physical space (e.g., one or more sensors 102-103)may include but are not limited to: force sensors, proximity sensors,motion sensors (e.g., an inertial measurement units (IMU), gyroscopes,and/or accelerometers), load sensors, position sensors, thermal imagingsensors, facial recognition sensors, depth sensors (e.g., RGB-D, laser,structured-light, and/or a time-of-flight camera), point cloud sensors,ultrasonic range sensors, infrared sensors, Global Positioning System(GPS) receivers, sonar, optical sensors, biosensors, Radio Frequencyidentification (RFID) systems, Near Field Communication (NFC) chip,wireless sensors, compasses, smoke sensors, light sensors, radiosensors, microphones, speakers, radars, touch sensors (e.g., capacitivesensors), cameras (e.g., color cameras, grayscale cameras, and/orinfrared cameras), and/or range sensors (e.g., ultrasonic and/orinfrared), among others.

Additionally, the sensors may be positioned within or in the vicinity ofthe physical space, among other possible locations. Further, an exampleimplementation may also use sensors incorporated within existing devicessuch as mobile phones, laptops, and/or tablets. These devices may be inthe possession of people located in the physical space such as consumersand/or employees within a retail space. Additionally or alternatively,these devices may be items on display, such as in a retail space usedfor selling consumer electronics. Yet further, each physical space 100may include the same combination of sensors or different combinations ofsensors.

FIG. 1 also depicts a computing system 104 that may receive data fromthe sensors 102-103 positioned in the physical space 100. In particular,the sensors 102-103 may provide sensor data to the computing system byway of communication link 120. Communication link 120 may include one ormore wired links and/or wireless links (e.g., using various wirelesstransmitters and receivers). A wired link may include, for example, aparallel bus or a serial bus such as a Universal Serial Bus (USB). Awireless link may include, for example, Bluetooth, IEEE 802.11 (IEEE802.11 may refer to IEEE 802.11-2007, IEEE 802.11n-2009, or any otherIEEE 802.11 revision), Cellular (such as GSM, GPRS, CDMA, UMTS, EV-DO,WiMAX, HSPDA, or LTE), or Zigbee, among other possibilities.Furthermore, multiple wired and/or wireless protocols may be used, suchas “3G” or “4G” data connectivity using a cellular communicationprotocol (e.g., CDMA, GSM, or WiMAX, as well as for “Wi-Fi” connectivityusing 802.11).

In other examples, the arrangement may include access points throughwhich one or more sensors 102-103 and/or computing system 104 maycommunicate with a cloud server. Access points may take various formssuch as the form of a wireless access point (WAP) or wireless router.Further, if a connection is made using a cellular air-interfaceprotocol, such as a CDMA or GSM protocol, an access point may be a basestation in a cellular network that provides Internet connectivity by wayof the cellular network. Other examples are also possible.

Computing system 104 is shown to include one or more processors 106,data storage 108, program instructions 110, and power source(s) 112.Note that the computing system 104 is shown for illustration purposesonly as computing system 104, but may include additional componentsand/or have one or more components removed without departing from thescope of the disclosure. Further, note that the various components ofcomputing system 104 may be arranged and connected in any manner.

Each processor, from the one or more processors 106, may be ageneral-purpose processor or a special purpose processor (e.g., digitalsignal processors, application specific integrated circuits, etc.). Theprocessors 106 can be configured to execute computer-readable programinstructions 110 that are stored in the data storage 108 and areexecutable to provide the functionality of the computing system 104described herein. For instance, the program instructions 110 may beexecutable to provide for processing of sensor data received from one ormore sensors 102-103.

The data storage 108 may include or take the form of one or morecomputer-readable storage media that can be read or accessed by the oneor more processors 106. The one or more computer-readable storage mediacan include volatile and/or non-volatile storage components, such asoptical, magnetic, organic or other memory or disc storage, which can beintegrated in whole or in part with the one or more processors 106. Insome implementations, the data storage 108 can be implemented using asingle physical device (e.g., one optical, magnetic, organic or othermemory or disc storage unit), while in other implementations, the datastorage 108 can be implemented using two or more physical devices.Further, in addition to the computer-readable program instructions 110,the data storage 108 may include additional data such as diagnosticdata, among other possibilities. Further, the computing system 104 mayalso include one or more power source(s) 112 configured to supply powerto various components of the computing system 104. Any type of powersource may be used such as, for example, a battery. In some embodiments,the computing system 104 may include more, fewer, and/or differentcomponents than those shown in FIG. 1.

FIGS. 2A and 2B display example embodiments of an environment with oneor more sensors. In FIG. 2A, the environment 200 a includes a sensor 202a , a sensor 203 a , and an actor 210 a at location 214 a . In FIG. 2B,the environment 200 b includes a sensor 202 b , a sensor 203 b , anactor 210 b at location 214 b , and a robotic device 230 b . In FIG. 2A,the direction of the actor 210 a is shown by vector 212 a , while thedirection of the face of the actor 217 a is shown by vector 218 a . InFIG. 2B, the direction of the actor 210 b is shown by vector 212 b ,while the direction of the face of the actor 217 b is shown by vector218 b . In FIGS. 2A and 2B, more, fewer, and/or different objects may beincluded in environments 200 a and/or 200 b.

The environments 200 a and 200 b displayed in FIGS. 2A and 2B maycorrespond to one or more physical spaces. In the displayed embodiments,the environment corresponds to one physical space, such as physicalspace 100 described in FIG. 1. The physical space may be used for avariety of purposes, including retail, manufacturing, assembly,distribution, business, healthcare, and/or other purposes. In otherembodiments, the environments 200 a and/or 200 b may include multiplephysical spaces, with each physical space having one or more sensors,such as sensors 102 and 103 described in FIG. 1. For example, a home maybe an environment with multiple rooms (bedroom, kitchen, bathroom,dining room, etc.) corresponding to multiple physical spaces, with eachphysical space having one or more sensors. Other embodiments ofenvironments 200 a and/or 200 b may also be possible.

In FIGS. 2A and 2B, the sensors 202 a and 202 b are LIDAR sensors usedto collect point cloud data of detected objects within the environment.Although a spinning LIDAR sensor is displayed, other types of sensors,including motion capture sensors, thermal imaging sensors, differenttypes of LIDAR sensors, or other depth sensors, may be used instead toobtain point cloud data or other types of data for detecting objects.While the displayed embodiments only show one point cloud data sensor,in other embodiments, multiple point cloud data sensors may be locatedthroughout the environment.

In FIGS. 2A and 2B, the sensors 203 a and 203 b are image capturedevices used to collect image data of locations in the environment. Theimage capture device may be a camera, including PTZ cameras(pan-tilt-zoom), stationary cameras, moving cameras, color cameras,grayscale cameras, and/or some other sensor that receives image data ofa location. While the displayed embodiments only show one sensorreceiving image data within the environment, in other embodiments,multiple sensors that receive image data of a location may be locatedthroughout the environment. Further, the sensors 202 a , 202 b , 203 a ,and 203 b may be stationary, moving, or some combination of the twowhile in the environment.

For example, sensor(s) 202 b and/or 203 b may be attached to a roboticdevice 230 b . In this case, when the robotic device 230 b isstationary, the attached sensor(s) 202 b and/or 203 b may also bestationary. However if the robotic device 230 b is moving, then theattached sensor(s) 202 b and/or 203 b would also be moving.Alternatively, the sensors may be attached to fixed locations within theenvironment, as shown by sensors 202 a , 202 b , 203 a , and 203 b inFIGS. 2A and 2B, respectively. Sensors 202 a and 202 b obtain pointcloud data of one or more detected actors at a location within theenvironment. Sensors 203 a and 203 b receive image data of one or morelocations within the environment.

In FIGS. 2A and 2B, an actor (210 a and 210 b , respectively) isdisplayed at a location (214 a and 214 b , respectively) within theenvironment. The actor may be stationary at one location, moving fromone location to another location, or a combination of both over a periodof time. If the actor is stationary at one location, the actor may besitting, standing, lying down, or stationary in some other way at alocation while oriented a particular direction. Alternatively, if theactor is moving from one location to another location, the actor may bewalking, running, jumping, or moving in some other way from one locationto another location along a particular direction. The actor may be aperson, a robotic device, or some other object that can face a directionor move along a direction.

In FIGS. 2A and 2B, the displayed actor has a corresponding direction212 a and 212 b , respectively. When the actor is stationary, thedirection corresponds to the direction the actor is oriented.Alternatively, when the actor is moving, the direction describes thedirection the actor is moving along. Directions 212 a and 212 b may berepresentative of a three-dimensional vector describing the directionthat the actor is oriented or moving in FIGS. 2A and 2B. Alternatively,directions 212 a and 212 b may describe a two-dimensional vectordescribing the direction that the actor is oriented or moving in FIGS.2A and 2B.

In FIGS. 2A and 2B, the face of the actor (217 a and 217 b ,respectively) has a corresponding direction 218 a and 218 b ,respectively. Based on image data received by sensors 203 a and 203 b ,the system 104 from FIG. 1 may determine a corresponding direction 218 aand 218 b , respectively, of the face of the actor. The system maydetermine the direction of the actor 212 a and 212 b based on thedetermined directions 218 a and 218 b , respectively. In the displayedembodiments, the direction vectors 218 a and 218 b are parallel todirection vectors 212 a and 212 b , respectively. In other embodiments,the directions 218 a and 218 b may be offset, or different, from thedirections 212 a and 212 b , respectively.

While image data of the face of the actor can be used to determine thedirection of the actor, image data of other parts of the actor may alsobe used. In the displayed embodiments, the actor has parts including ahead, a body, one or more arms, and one or more legs. An actor mayinclude more, fewer, and/or different parts than those described. Insome embodiments, image data of one or more of these parts of the actormay be used to determine the direction of the actor. Alternatively,image data of the face of the actor may be used to determine information(such as direction) about one or more of the parts of the actor.

FIG. 2B displays robotic device 230 b . The operation of the roboticdevice 230 b may be adjusted based on the determined direction 212 b ofthe actor 210 b . The robotic device 230 b may adjust operationsincluding navigation of the robotic device 230 b , teleconferencingbetween the robotic device 230 b and the actor 210 b , telepresence of arobotic device user with the actor 210 b , or the execution of one ormore tasks. Further, in response to the determined direction 212 b ofthe actor 210 b , the robotic device 230 b may adjust its operation bydoing nothing and/or stopping what the robotic device 230 b waspreviously doing. Other operation adjustments by the robotic device 230b are also possible.

The operation of the robotic device 230 b may be adjusted based onvarious data besides, or in addition to, the direction of the actor. Forexample, robotic device operation may be adjusted based on the directionof the body of the actor, the direction of the face of the actor, thedirection of a different part of the actor, historical informationindicating previous directions of the actor, or other data. Roboticdevice operation may be adjusted based on a combination of theaforementioned data. Other criteria are also possible for adjustingrobotic device operation.

The system 104 of FIG. 1 may provide information indicating thedetermined direction 212 b to a user/operator that is controlling orassisting the robotic device 230 b . The system 104 may rely on acommunication link (such as link 120) in connection with a computingdevice (not shown) of the user or operator of the robotic device 230 b .The computing device may be a computer, personal computer, laptop,phone, PDA, tablet, mobile device, wearable computing device, or someother computing device of the user or operator. Other embodiments forproviding the information to the user/operator controlling or assistingthe robotic device are also possible.

Sensors 203 a and/or 203 b may be adjusted based on the determineddirection of the actor 212 a and/or 212 b , respectively. Sensors 203 aand/or 203 b may zoom, pan, tilt, or adjust in some other manner inresponse to the direction of the actor. One or more sensors 203 a and/or203 b may be adjusted in response to the determined direction of theactor. Other adjustments of the sensors 203 a and/or 203 b based on thedirection of the actor may also be possible.

Although FIG. 2B displays sensor 202 b as being remotely located fromthe robotic device 230 b , in some embodiments, robotic device 230 b mayinclude one or more sensors 202 b and/or 203 b to detect actor 210 b .In some embodiments, the robotic device 230 b uses its own attachedsensors to detect and determine the direction of the actor 210 b .Alternatively, the robotic device 230 b may receive communications fromsystem 104 (see FIG. 1) indicating the direction of the actor 210 b .Alternatively, the robotic device 230 b may receive sensor data fromsystem 104 (see FIG. 1) and then determine the direction 212 b of theactor 210 b . Other methods of determining the direction of the actor210 b for the robotic device 230 b are also possible.

In some embodiments, the robotic device 230 b may be the detected actor.In these cases, the robotic device may have characteristics similar tothat of the detected actor 210 b . For example, the robotic device 230 bmay be stationary, moving, or a combination of both over a period oftime. The direction of the robotic device may be similar to thedirection 212 b of the detected actor 210 b . In particular, thedirection of the robotic device 230 b as a detected actor may correspondto the direction the robotic device is oriented, the direction therobotic device is moving along, or some other direction of the roboticdevice. Furthermore, the robotic device direction may be determinedbased on image data of a face of the robotic device. Also, the systemmay determine a direction of the face of the robotic device based on theimage data of the face of the robotic device.

FIG. 3A displays an example point cloud representative of an actorwithin an environment, according to an embodiment. FIG. 3A includespoint cloud data 300 a representative of an actor within an environment.The received point cloud data includes various portions of point clouddata 301 a , 302 a , and 303 a . The point cloud data 300 a may bereceived at a location 314 a within the environment. In otherembodiments, the point cloud may include more, fewer, and/or differentportions of point cloud data. Additionally, the point cloud data may bereceived for an actor at different locations or more locations than thelocation shown for FIG. 3A.

Point cloud data 300 a may include portions of point cloud datarepresentative of different parts of the actor. Point cloud data 301 amay be representative of the head of the actor, while point cloud data302 a may be representative of the body of the actor. In FIG. 3A, thebody of the actor may include the actor's arms, legs, torso, and/orother parts. However in other embodiments, the body may refer to fewerparts of the actor (such as the torso) while other parts of the actormay be considered separate portions of point cloud data (such as thearms, legs, etc.).

The point cloud data 300 a includes a plurality of points received fromone or more sensors within an environment, such as sensors 102, 202 a ,and/or 202 b from FIGS. 1, 2A, and 2B, respectively. Each received pointmay represent a point on the surface of the actor. The sensor mayprovide a cluster of points in a particular area of the actor. Thecluster of points may then be representative of a part of the actor. Forexample, the cluster of points identified by 301 a may be representativeof the head of the actor.

Determining information about the actor based on the received, clusteredpoint cloud data 300 a may be challenging for at least three reasons.First, the point cloud data received from the one or more sensors may besparse. Thus, the point cloud data may not be as rich, as detailed, orhave as many points as other sensor data for determining informationabout an actor.

Second, the point cloud data may have a non-uniform density. Somestripes of received point cloud data may have a high density. But otherstripes of received point cloud data may have a low density. Thus,techniques for determining additional information based on the receivedpoint cloud data may accommodate point cloud data with varying densityvalues.

Third, the received point cloud data may be prone to blind spots. Blindspots occur when a portion of the environment cannot be sensed by theone or more sensors (such as sensors 102, 202 a , and/or 202 b displayedin FIGS. 1, 2A, and 2B, respectively) within the environment. A blindspot may occur because a sensor is not present at a portion of theenvironment.

Alternatively, blind spots may occur due to obstacles and/or occlusions.For example, a blind spot may occur at a portion of the environment dueto an object blocking a portion of the environment from sensing by asensor. For another example, a blind spot may occur at a portion of theenvironment because another actor or robotic device is located inbetween the sensor and the portion of the environment. Thus, if an actorwas located at a portion of the environment while the robotic device (oranother actor) was located in between the sensor and the actor, therobotic device may cause the portion of the environment to become ablind spot. The blind spot may prevent the actor from being detected bythe sensor. Additional sensors may be added to the environment to reduceblind spots.

Because the received point cloud data is sparse, has a non-uniformdensity, and is prone to blind spots, it can be challenging to determineadditional information about an actor based on the received point clouddata. Thus, techniques for determining information about an actor (suchas the direction of the actor) using the point cloud data mayaccommodate the characteristics and challenges of the point cloud data.One technique that accommodates these challenges is to collect andprocess image data of the location (such as location 314 a ) of theactor. The image data may be processed in combination with the pointcloud data to determine the direction of the actor.

FIG. 3B displays example image data representative of a location withinan environment, according to an example embodiment. FIG. 3B displaysimage data 310 b representative of a location 314 b . The image data 310b includes image data of the actor 300 b . The image data of actor 300 bincludes image data for parts of the actor, including image data 301 b,302 b , 303 b , and 304 b . In other embodiments, the image data mayinclude fewer, more, and/or different image data. Additionally, theimage data may be received from a different location or more locationsthan the location shown in FIG. 3B.

Image data 310 b displays location 314 b . Location 314 b may be withina physical space of the environment. Alternatively, location 314 b mayspan multiple physical spaces within the environment. The image data oflocation 314 b may also include portions, or all, of one or more objects(such as actors) at the location 314 b.

In FIG. 3B, image data 310 b includes image data for an actor 300 b atlocation 314 b . In some embodiments, the image data 300 b may include aportion, or all, of the actor 300 b . In the displayed embodiment, imagedata 300 b includes image data 301 b representative of the head of theactor and image data 302 b representative of the body of the actor.Image data 301 b includes image data 303 b representative of the face ofthe actor and image data 304 b representative of one or more facialfeatures of the actor. Image data of the actor may be representative ofmore, fewer, and/or different parts of an actor than what is displayedin FIG. 3B. Image data 310 b may also include image data for multipleactors at location 314 b , in some embodiments.

When the image data 310 b includes one or more objects (such as actors)at location 314 b , the system may also have received point cloud datafor some or all of the objects at location 314 b . For objects where thesystem has received point cloud data, the corresponding image data forthe objects may be mapped to point cloud data for the one or moreobjects within the image data. The mapping may be done based on thelocation 314 b . Other methods of mapping image data for objects (suchas actors) at a location 314 b to corresponding point cloud data for theobjects may also be possible.

For example, referring to FIGS. 3A and 3B, the system (such as system104 from FIG. 1) may receive image data 310 b for location 314 bincluding image data 300 b representative of an actor at location 314 b. At a same or similar time, the system may also receive point clouddata 300 a representative of an actor at location 314 a . The location314 a may be the same as, or nearby, location 314 b . The system maydetermine that the actor at location 314 b and the actor at location 314a are the same actor. Thus, the system may map the image data 300 brepresentative of the actor at location 314 b to the point cloud data300 a representative of the actor at location 314 a . By mapping theimage data 300 b to point cloud data 300 a the system may then makeinferences about point cloud data 300 a to determine information aboutthe actor.

The system may determine the direction of an actor based on image datarepresentative of the actor that is mapped to point cloud datarepresentative of the actor. The system may first determine that themapped image data is of a location that is the same as, or similar to,the location of the point cloud data. A portion of the image data thatincludes a face of the actor may then be identified. The system may thendetermine a direction of the face of the actor based on the image data.The system may next determine a direction of the actor based on thepoint cloud data and/or the determined direction of the face of theactor. The system may determine the direction of the actor using varioustechniques.

Referring to FIGS. 3A and 3B, in one example, the system identifies theface of the actor based on image data 301 b representative of the headof the actor, image data 303 b representative of the face of the actor,and image data 304 b representative of features of the face of theactor. After determining the face of the actor based on the image data,the system may then locate the point cloud data that corresponds to thehead of the actor, 301 a . Once the point cloud data 301 a isidentified, the system may then determine the point cloud data 302 athat is representative of the body of the actor.

After identifying point cloud data 302 a , the system may use a boundingbox that surrounds a portion, or all of, the point cloud data 302 a todetermine the direction of the body of the actor 302 a . The directionof the actor may be inferred to be the same as the direction of the bodyof the actor by the system. Thus, the system determines the direction ofthe actor by using image data 300 b to identify point cloud datarepresentative of the body of the actor 302 a.

In another example, after mapping the image data 301 b to the pointcloud data 301 a , the system may identify the face of the actor basedon image data 301 b , 303 b , and 304 b . After determining the face ofthe actor, the system may then determine a direction of the face of theactor based on image data 301 b , 303 b , and 304 b . In embodimentswhere the system infers that the direction of the face of the actor isthe same orientation as the direction of the body of the actor, thesystem may determine the direction of point cloud data 302 a to be thesame orientation as the determined direction of the face of the actor.The system may also infer that the direction of the actor is the sameorientation as the direction of the body after the actor. Thus, thesystem may determine the direction of the actor is the same orientationas the direction of the face of the actor.

In another example, historical information may be used to determine acurrent direction of the actor. Historical information may include oneor more previous directions of the actor. The system may determine adirection of the actor based on received image data, as described inearlier examples. The system may then use historical information ofprevious directions of the actor to update, and/or confirm, a currentdetermined direction of the actor. Thus, various additional data, suchas image data, historical information, and/or other data, can enableinferences about the point cloud data to determine information about theactor, such as the direction of the actor.

Determining inferences can be helpful when the actor conducts movementsdifferent from walking forward. For example, the additional data, andinferences that can result from the additional data, can help withdetermining the direction of the actor when the actor turns, turns hishead in a direction different from his body, steps sideways, or doessome other movement other than walking forward with his head facingforward. The additional information (such as image data) can also helpwith determining information about the actor who an actor standingstill. Image data may be periodically acquired to improve accuracy ofthe determined information about the actor.

For example, the system may periodically acquire image data 310 b toacquire updated image data of the face of the actor. The updated imagedata may then allow the system to update the determined direction of theface of the actor, the determined point cloud data 302 a , historicalinformation, or other data used for determining the direction of theactor. The system may then update the determined direction of the actorbased on the newly acquired image data 310 b . The system may acquireimage data 310 b to be used with point cloud data 300 a to improve aconfidence of the determined direction of the actor. The confidence maydescribe the probability that the determined direction of the actor iscorrect and may be expressed as a confidence value. Updated image data310 b can be useful for situations where the orientations of thedirections of the actor and the face of the actor are the same (seeFIGS. 2A and 2B) or where the orientations are different, as shown inFIG. 4.

FIG. 4 illustrates an example environment with an actor, according to anembodiment. FIG. 4 displays environment 400 which contains sensor 402,sensor 403, and actor 410. The actor 410 may be located at location 414while having a direction 412 and a potential direction 413. The actor410 may include a head 416, which includes a face 417. The head 416and/or face 417 of the actor 410 may be oriented along direction 418.The environment 400 may include more, fewer, and/or different sensors,actors, and/or directions than those displayed in FIG. 4.

In FIG. 4, the actor 410 is walking along a direction 412. The head ofthe actor 416 and the face of the actor 417 are oriented along adirection 418. The direction 418 may be different from the direction412. In one embodiment, the direction 418 may be offset 15°counterclockwise from direction 412. In the displayed embodiment, imagedata representative of the actor 410 may be used by the system (such assystem 104) to make inferences about point cloud data representative ofthe actor 400. For example, the system may update confidence values ofpotential directions of the actor using image data representative of theactor 410.

In the displayed embodiment, the system may initially receive pointcloud data representative of the actor 410 and determine that the actor410 may be oriented along directions 412 or 413. Specifically, thesystem may analyze point cloud data representative of actor 410 toidentify parts of the actor, such as the head, torso, legs, or otherparts. The system may initially determine potential directions 412 and413 of the actor 410 based on the location and orientation of theidentified parts of the actor. For example, based on the location andorientation of the arms, legs, head, and/or torso within point clouddata for actor 410, the system may determine that the actor is orientedalong direction 412 or direction 413. In some embodiments, the systemmay use historical information (such as previous directions of theactor) to determine and/or update potential directions 412 and 413 ofthe actor 410.

Direction 413 may be offset 180° from direction 412, and thus, orientedin the opposite direction. Accordingly, the system may initially assigna confidence value of 50% to direction 412 and a confidence value of 50%to direction 413. Thus, the point cloud data initially indicates thatactor 410 has a similar or equal likelihood of being oriented alongeither direction 412 or direction 413. Accordingly, additional data maybe needed to determine if the actor 410 is oriented along direction 412or direction 413.

The system may subsequently receive image data representative of theface of the actor 417 and determine that the direction of the face ofthe actor is oriented along direction 418. The system may also determinethat the direction 418 is offset 15° counterclockwise from the direction412. Because it may be unlikely, or impossible, for the direction of theface of the actor 417 to be offset from the direction of the torso theactor by 165°, the system may lower the confidence value for direction413 and increase the confidence value for, direction 412. For example,the system may lower the confidence value of direction 413 from 50% to10% and increase the confidence value of direction 412 from 50% to 90%.The system may then determine the actor 410 is oriented along direction412 due to the higher confidence value (90%) than that of direction 413(10%).

Confidence values of one or more potential directions of actors can beupdated based on various data. For example, the system may useperiodically acquired image data, historical information (such asprevious directions of the actor), point cloud data, and/or other datato determine one or more confidence values for one or more potentialdirections of the actor (such as directions 412 and 413). The system maythen determine the actor is oriented along the direction with thehighest confidence value (such as direction 412). Although confidencevalues have been described using percentage values, other units, scales,scores, ratings, or types of measurements may be used to indicate thelikelihood of a potential direction of an actor. Other embodiments ofthe system determining the direction of the actor based on image dataand point cloud data are also possible.

FIG. 5 illustrates a flowchart showing the method 500 that may allow fordetermining the direction of an actor based on sensors in anenvironment, according to an example embodiment. The method 500 may beexecuted by a control system, such as computing system 104 shown inFIG. 1. Alternatively, the method may be executed by a robotic device,such as the robotic device 230 b displayed in FIG. 2B. Other devices orsystems may execute method 500 in other embodiments.

Furthermore, it is noted that the functionality described in connectionwith the flowcharts described herein can be implemented asspecial-function and/or configured general-function hardware modules,portions of program code executed by a processor for achieving specificlogical functions, determinations, and/or steps described in connectionwith the flowchart shown in FIG. 5. Where used, program code can bestored on any type of computer-readable medium, for example, such as astorage device including a disk or hard drive.

In addition, each block of the flowchart shown in FIG. 5 may representcircuitry that is wired to perform the specific logical functions in theprocess. Unless specifically indicated, functions in the flowchart shownin FIG. 5 may be executed out of order from that shown or discussed,including substantially concurrent execution of separately describedfunctions, or even in reverse order in some examples, depending on thefunctionality involved, so long as the overall functionality of thedescribed method is maintained.

As shown by block 502 of FIG. 5, method 500 may involve receiving pointcloud data for an actor at a first location in an environment, whereinthe point cloud data includes a plurality of points representative ofthe actor. In some examples, the point cloud data may be received by oneor more LIDAR sensors attached at a fixed location within theenvironment, located on a mobile robot in the environment, and/orlocated elsewhere in the environment. In other examples, the point clouddata may include a plurality of points which represent the surface ofthe detected actor. In some other examples, the received point clouddata may be sparse with a non-uniform density. In additional examples,techniques may be used to accommodate the sparse point cloud data with anon-uniform density.

Method 500 may further involve receiving image data corresponding to thepoint cloud data for the actor based on image data being representativeof the first location in the environment, as displayed by block 504 inFIG. 5. In some examples, portions, or all, of the image data may bemapped to corresponding point cloud data. In additional examples, theimage data may be received by one or more cameras. In other examples,the one or more cameras may be attached at a fixed location within theenvironment, located on a mobile robot in the environment, and/orlocated elsewhere in the environment.

Method 500 may also involve identifying a portion of the received imagedata that is representative of a face of the actor, as displayed byblock 506 in FIG. 5. In some examples, features of the face of the actormay be used to identify the image data representative of the face of theactor. In additional examples, point cloud data representative of a bodyof the actor may be identified after identifying a face of the actor. Inother examples, a bounding box surrounding a portion, or all, of thepoint cloud data representative of the body of the actor may be used todetermine information about the actor (such as the direction of theactor). In some other examples, image data representative of the face ofthe actor may be periodically acquired to update the informationdetermined about the actor.

The method 500 may additionally involve determining a direction of theface of the actor based on the identified portion of the received imagedata, as shown by block 508 in FIG. 5. In some examples, the directionof the face of the actor may be used to infer the direction of theactor. In additional examples, the direction of the actor may beinferred to be the same as the direction of the face of the actor.

Method 500 may also include determining a direction of the actor basedon the direction of the face of the actor, as can be seen by block 510in FIG. 5. In some examples, the direction of the actor may bedetermined based on one or more confidence values of one or morepotential directions of the actor. In additional examples, the potentialdirection of the actor with the highest confidence value may be selectedas the direction of the actor. In some other examples, historicalinformation may be used in combination with the image data and pointcloud data to determine a current direction of the actor.

Method 500 may also involve providing information indicating thedetermined direction of the actor, as shown by block 512 in FIG. 5. Insome examples, information indicating the direction of the actor may beprovided to a user or an operator of a robotic device. In additionalexamples, the user or operator may adjust the operation of the roboticdevice based on the provided information indicating the direction of theactor. In other examples, the robotic device may be the actor detectedwithin the environment.

Although not displayed in FIG. 5, method 500 may include additionalsteps, such as adjusting operation of a robot based on the determineddirection of the actor. In some examples, the navigation of the robotmay be adjusted based on the determined direction of the actor. However,the robot could be adjusted to operate in a different way in response tothe determined direction of the actor.

Various applications and environments using sensors to determine thedirection of an actor in the environment are possible for the disclosedsystems and methods. For example, some environments where determinationof the direction of an actor within the environment may be applicableinclude manufacturing facilities, mailing or shipping facilities,airports, hospitals, or other environments employing sensors fordetecting actors. Furthermore, other applications where determination ofthe direction of an actor within an environment may be applicableinclude construction, shipping, manufacturing, healthcare, and/or otherapplications using environments with sensors. Other applicableenvironments and applications for the disclosed systems and methods mayalso be possible.

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its spirit and scope, as will be apparentto those skilled in the art. Functionally equivalent methods andapparatuses within the scope of the disclosure, in addition to thoseenumerated herein, will be apparent to those skilled in the art from theforegoing descriptions. Such modifications and variations are intendedto fall within the scope of the appended claims.

The above detailed description describes various features and functionsof the disclosed systems, devices, and methods with reference to theaccompanying figures. In the figures, similar symbols typically identifysimilar components, unless context dictates otherwise. The exampleembodiments described herein and in the figures are not meant to belimiting. Other embodiments can be utilized, and other changes can bemade, without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

A block that represents a processing of information may correspond tocircuitry that can be configured to perform the specific logicalfunctions of a herein-described method or technique. Alternatively oradditionally, a block that represents a processing of information maycorrespond to a module, a segment, or a portion of program code(including related data). The program code may include one or moreinstructions executable by a processor for implementing specific logicalfunctions or actions in the method or technique. The program code and/orrelated data may be stored on any type of computer readable medium suchas a storage device including a disk or hard drive or other storagemedium.

The computer readable medium may also include non-transitory computerreadable media such as computer-readable media that stores data forshort periods of time like register memory, processor cache, and randomaccess memory (RAM). The computer readable media may also includenon-transitory computer readable media that stores program code and/ordata for longer periods of time, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. A computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a block that represents one or more information transmissionsmay correspond to information transmissions between software and/orhardware modules in the same physical device. However, other informationtransmissions may be between software modules and/or hardware modules indifferent physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A method comprising: receiving point cloud datafor an actor at a first location in an environment, wherein the pointcloud data includes a plurality of points representative of the actor;receiving image data corresponding to the point cloud data for the actorbased on the image data being representative of the first location inthe environment; identifying a portion of the received image data thatis representative of a part of the actor; determining a direction of theactor based on the identified portion of the received image data that isrepresentative of the part of the actor; and providing informationindicating the determined direction of the actor.
 2. The method of claim1, further comprising: determining a plurality of potential directionsof the actor based on the point cloud data; and determining thedirection of the actor from the plurality of potential directions of theactor based on the identified portion of the received image data that isrepresentative of the part of the actor.
 3. The method of claim 2,further comprising: determining a plurality of confidence valuescorresponding to the plurality of potential directions of the actor; andadjusting the plurality of confidence values based on the identifiedportion of the received image data that is representative of the part ofthe actor.
 4. The method of claim 1, further comprising: determining adirection the actor is moving based at least on the received point clouddata and the identified portion of the received image data that isrepresentative of the part of the actor; and providing informationindicative of the direction the actor is moving.
 5. The method of claim1, further comprising: receiving historical information indicating oneor more previous directions of the actor; determining a currentdirection of the actor based at least on the received point cloud data,the historical information, and the identified portion of the receivedimage data that is representative of the part of the actor; andproviding information indicating the current direction of the actor. 6.The method of claim 1, further comprising: determining a bounding boxsurrounding a portion of the point cloud data; and determining thedirection of the actor based on the bounding box.
 7. The method of claim1, further comprising: determining a direction of a body of the actorbased at least on the received point cloud data and the identifiedportion of the received image data that is representative of the part ofthe actor; and providing information indicative of the direction of thebody of the actor.
 8. The method of claim 7, further comprisingdetermining a direction of the part of the actor based on the identifiedportion of the received image data that is representative of the part ofthe actor, wherein the direction of the body of the actor is differentfrom the determined direction of the part of the actor.
 9. The method ofclaim 1, wherein the image data is received from one or more imagecapturing devices, the method further comprising adjusting the one ormore image capturing devices based at least on the determined directionof the actor.
 10. The method of claim 1, wherein the point cloud data isreceived from a plurality of LIDAR sensors attached at fixed locationsin the environment such that the point cloud data has non-uniformdensity.
 11. The method of claim 1, wherein the part of the actorcomprises one of a head, a body, a leg, or an arm.
 12. A non-transitorycomputer-readable medium storing instructions that are executable by oneor more computing devices, wherein executing the instructions causes theone or more computing devices to perform functions comprising: receivingpoint cloud data for an actor at a first location in an environment,wherein the point cloud data includes a plurality of pointsrepresentative of the actor; receiving image data corresponding to thepoint cloud data for the actor based on the image data beingrepresentative of the first location in the environment; identifying aportion of the received image data that is representative of a part ofthe actor; determining a direction of the actor based on the identifiedportion of the received image data that is representative of the part ofthe actor; and providing information indicating the determined directionof the actor.
 13. The non-transitory computer-readable medium of claim12, wherein executing the instructions further causes the one or morecomputing devices to perform additional functions comprising:determining a direction of the part of the actor based on the identifiedportion of the received image data; and determining the direction of theactor based on the determined direction of the part of the actor. 14.The non-transitory computer-readable medium of claim 12, whereinexecuting the instructions further causes the one or more computingdevices to perform additional functions comprising: determining adirection of the part of the actor based on the identified portion ofthe received image data; determining a direction the actor is movingbased at least on the received point cloud data and the direction of thepart of the actor; and providing information indicative of the directionthe actor is moving.
 15. The non-transitory computer-readable medium ofclaim 12, wherein executing the instructions further causes the one ormore computing devices to perform additional functions comprising:determining a direction of the part of the actor based on the identifiedportion of the received image data; determining a direction of a body ofthe actor based at least on the received point cloud data and thedirection of the part of the actor; and providing information indicativeof the direction of the body of the actor.
 16. A robotic devicecomprising: one or more processors; and a memory storing instructionsthat when executed by the one or more processors cause the roboticdevice to perform functions comprising: receiving point cloud data foran actor at a first location in an environment, wherein the point clouddata includes. a plurality of points representative of the actor;receiving image data corresponding to the point cloud data for the actorbased on the image data being representative of the first location inthe environment; identifying a portion of the received image data thatis representative of a part of the actor; determining a direction of theactor based on the identified portion of the received image data that isrepresentative of the part of the actor; and adjusting operation of therobotic device based on the determined direction of the actor.
 17. Therobotic device of claim 16, wherein the instructions further cause therobotic device to perform functions comprising: determining a directionof the part of the actor based on the identified portion of the receivedimage data that is representative of the part of the actor; determininga direction the actor is moving based at least on the received pointcloud data and the direction of the part of the actor; and adjustingoperation of the robotic device based on the direction the actor ismoving.
 18. The robotic device of claim 16, wherein the instructionsfurther cause the robotic device to perform functions comprising:determining a confidence level associated with the determined directionof the actor; and periodically adjusting the confidence level based onreceived image data.
 19. The robotic device of claim 16, wherein theinstructions further cause the robotic device to perform functionscomprising: navigating the robotic device based at least on thedetermined direction of the actor.
 20. The robotic device of claim 16,wherein the instructions further cause the robotic device to performfunctions comprising: receiving historical information indicating one ormore previous directions of the actor; determining a current directionof the actor based at least on the received point cloud data, thehistorical information, and the identified portion of the received imagedata that is representative of a part of the actor; and adjustingoperation of the robotic device based on the current direction of theactor.